Linkage in Evolutionary Computation 2009 EV |
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Call for Book Chapters Title: Exploitation of Linkage Learning in Evolutionary Algorithms (to be published by Springer) Overview Genetic and evolutionary algorithms (GEAs) are powerful search methods based on the paradigm of evolution and widely applied to solve problems in many disciplines. In order to improve the performance and applicability, numerous sophisticated mechanisms have been introduced and integrated into GEAs in the past decades. One major category of these enhancing mechanisms is the concept of linkage, which models the relation between the decision variables with the genetic linkage observed in biological systems, and linkage learning techniques. Linkage learning connects the computational optimization methodologies and the natural evolution mechanisms. Not only can learning and adapting natural mechanisms enable us to design better computational methodologies, but also the insight gained by observing and analyzing the algorithmic behavior permits us to further understand biological systems, based on which GEAs are developed. Scope This edited volume aims at reviewing of current state-of-art linkage learning techniques, exchanging of ideas and viewpoints on linkage, as well as discussing the future directions. We invite researchers to submit their original work related to, but not limited to, the following topics:
Submission Guidelines Manuscripts should be prepared according to the series book format provided by Springer, and the page limit for each chapter is 30 pages. Prospective authors are invited to indicate the intention to contribute by describing the topic, organization, expected number of pages, and contact author of the chapter. Please direct all correspondences to the editor. Important Dates Indication of Intention to Contribute: Submission of Manuscripts: Submission of Revised Manuscripts: Submission of the Final Camera-ready Manuscript (to Springer): December 18, 2009 Editor
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Last Updated on Tuesday, 15 December 2009 11:46 |